Crowd flow forecast based on SOM neural network in application of energy-saving lighting

DOI

10.2991/eeeis-16.2017.89How to use a DOI?

Keywords

Artificial intelligence; Energy-saving; SOM network; Lighting-system

Abstract

Generally the common lights-control is set in the mode of preset time control, which cannot be adjusted and would not meet the requirements of fine and smart control, so the plug-in of Artificial intelligence algorithms can make a big change of common made. SOM (self-organizing feature map network) is used for flow data feature extraction and classification, and the results are used as inputs of BP network to make a short-term forecast, then combined with the intensity of illumination requirements to output the different rank which is as the same level of flow data, at the end the comparison with traditional way is made in terms of energy saving to prove the advantage of this method of paper. The experimental results show that flow data forecast based on the SOM combined with BP neural network prediction of short-term is more reliable than original BP network, has a better effect in energy-saving, and has provided the new energy conservation plan for the lighting system

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jun-Feng Xu
AU  - Li-Xin Ma
PY  - 2016/12
DA  - 2016/12
TI  - Crowd flow forecast based on SOM neural network in application of energy-saving lighting
BT  - Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016)
PB  - Atlantis Press
SP  - 724
EP  - 729
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-16.2017.89
DO  - 10.2991/eeeis-16.2017.89
ID  - Xu2016/12
ER  -